• Modern molecular biology has progressed rapidly on based primarily on the development of high throughput assays that allow us to measure tens of thousands to millions of features across large populations (e.g. cells, individuals).

• As a result we typically have data represented in arrays with N rows and M columns, where N and M are often very large.

• We are often interested in finding out if there are lower dimensional representations of the data that can be used to understand the relationships that exist.

• In this lecture we will explore some of the tools and try to get a basic understanding of how these methods work and their strengths and weaknesses.

• what is an embedding…

• low dimensional representations of distances

• Principal Component Analysis

• Modern Statistics for Modern Biology (Holmes and Huber); https://www.huber.embl.de/msmb/
• Elements of Statistical Learning (Hastie, Tibshirani and Friedman) https://hastie.su.domains/Papers/ESLII.pdf
• Modern Applied Statistics with S. Fourth Edition, by W. N. Venables and B. D. Ripley; MASS package in R
• CRAN https://cran.r-project.org/ Task Views
• Wikipedia pages are pretty good for a simple introduction, but often do not provide appropriate guidance/interpretation

• there are very specific mathematical definitions
• we will provide general thoughts/abstractions rather than abstract math
• the idea of an vector space embedding is to take high dimensional data and represent it as an element in some \(p\) dimensional space
• we will use \(n\) to represent the number of items we want to embed and usually use that to index the rows.
• in mathematics we might start with a matrix X that has \(n\) rows and \(p\) columns and we want to find a representation as a different matrix Y with n rows and \(k << p\) columns
• for embeddings like UMAP and t-SNE usually we consider k = 2 or 3

• you will see a number of examples of classical machine learning
• classification or supervised ML - we have a labeled set of data and want to use it to apply labels to other observations where we only have features
• clustering or unsupervised ML - we have data with measured features and want to group similar individuals together
• these methods rely on some notion of similarity that can be defined between any pair of objects
• all pairwise distances between entities can be represented in a distance matrix, \(D\)

• we start with data that is \(X\) a matrix with M rows (one per observation) and \(p\) columns (represent features measured on each of the observations)
• distances between all pairs form a matrix with \(M\) rows and \(M\) columns
• we want to find a representation for the data \(Y\) which is say \(M \times 2\) or perhaps 3
• so we can plot these lower dimensional points to visualize the data but where the distances between the points in \(Y\) are close to the distances based on the observed data

• we want to find a low dimensional representation where the distances between points in this representation are close to those in the original measurement space

• but these cannot be accurate - some corners need to be cut

• some methods emphasize distances between similar objects and do not try to constrain dissimilar objects (as long as the distances remain big)

• warning you typically cannot use these transformed observations for downstream analyses

• many methods and options discussed in the references

• and the tools I will go into more detail on (PCA) can usefully be adapted to understanding the distance matrices

• t Stochastic neighbor embedding - another dimension reduction method

• designed with very similar objectives - find a low dimensional projection (embedding) of the data that preserves some notion of similar

• divide the data into things that are near each other and things that are far apart

• spend more effort maintaining consistent distances for those things that are near each other

• designed to have good performance on large data sets

• Uniform manifold approximation and projection

• it assumes that the data is uniformly distributed on a locally connected Riemannian manifold and that the Riemannian metric is locally constant or approximately locally constant (Wikipedia)

• good performance, based on a formalism

• there seems to be little research or practical approaches to answering questions of the form: “how well does the dimensionality reduction work?”

• we might want to know if there are specific features of the data that were obscured by the dimension reduction method being used

• some of the tools in the next section might be helpful

• looking at Pearson or Spearman correlations between the distances between observations in the original space versus the reduced dimension space

• it seems like their are some opportunites to look for tools that can help ensure that these methods are performing approximately as inteded

Principal Components Analysis is a multivariate method that helps you understand the structure of data that have been collected on \(N\) individuals and \(p\) variables. Each individual can be represented by a point in \(p\) dimensional space. Our motivating example will be test scores on \(N=88\) students across \(p=5\) topics.The first two exams were closed book, the last 3 open book.

##   mechanics vectors algebra analysis statistics
## 1        77      82      67       67         81
## 2        63      78      80       70         81
## 3        75      73      71       66         81

A motivating question is how should the exam scores be combined to give an overall score? One answer is to use the mean, but is there something better?

• We will refer to the data matrix as \(\bf{X}\), and each row as \(\bf{x_i}\) = \((x_{i,1}, x_{i,2}, \ldots,x_{i,p})\).
• We can see in the picture that we could rotate the coordinate axis to align differently with the data
• There are many possible rotations, but we want to consider one where the axis are oriented towards directions of greatest variation in the point cloud.

• If we rotate the coordinate axis, as in the plot, the relationship between the data points has not changed at all.

• The data points are still described by a matrix (\(\bf{X^\prime}\)) that has \(N\) rows and \(p\) columns.

• The PCs are chosen to be orthogonal, just like our original coordinate system

Chapter 7 of MSMB (https://www.huber.embl.de/msmb/07-chap.html) has many other examples and a slightly different approach.

They cover linear regression, other decompositions and do some really thorough explanations of the singular value decomposition that underlies this.

My main source: Multivariate Analysis, Mardia, Kent, Bibby (1979)…which is still relevant and accurate today…

• The PCs provide us with an alternative set of coordinates.
• The first PC corresponds to the direction of most variation in the \(\bf{x}\)’s. Note that rescaling the \(\bf{x}\)’s would clearly affect the PCs.
• After we compute the PCs we have three quantities of interest:
  1. The variability in each of the new directions (eigenvalues).
  2. The vectors (eigenvectors) that are used to form the new coordinates (they are linear combinations of the original points).
  3. The new set of features, the rotated values.

Since we have five covariates, each student’s score can be represented in 5-D space. The principal components give us a different coordinate system than the one based on the exam scores.

## Standard deviations (1, .., p=5):
## [1] 26.210490 14.216577 10.185642  9.199481  5.670387
## 
## Rotation (n x k) = (5 x 5):
##                   PC1         PC2        PC3          PC4         PC5
## mechanics  -0.5054457 -0.74874751  0.2997888  0.296184264  0.07939388
## vectors    -0.3683486 -0.20740314 -0.4155900 -0.782888173  0.18887639
## algebra    -0.3456612  0.07590813 -0.1453182 -0.003236339 -0.92392015
## analysis   -0.4511226  0.30088849 -0.5966265  0.518139724  0.28552169
## statistics -0.5346501  0.54778205  0.6002758 -0.175732020  0.15123239

The rotation matrix, in the previous slide tells you how to compute the new, 5 dimensional values. The first value is the linear combination where the \(l_i\) come from PC1, the second from PC2, and so on.

So we can compute a new \(n\) by \(k\) matrix, where for each of the \(k\) dimensions we can compute the new value for each individual. This gives us a set of five columns (rows are people, columns are variables) that are equivalent to the original data. The points are in some sense identical, just we have changed coordinate systems.

Let’s have a look at the coefficients - these are sometimes called rotations.

##                   PC1         PC2        PC3          PC4         PC5
## mechanics  -0.5054457 -0.74874751  0.2997888  0.296184264  0.07939388
## vectors    -0.3683486 -0.20740314 -0.4155900 -0.782888173  0.18887639
## algebra    -0.3456612  0.07590813 -0.1453182 -0.003236339 -0.92392015
## analysis   -0.4511226  0.30088849 -0.5966265  0.518139724  0.28552169
## statistics -0.5346501  0.54778205  0.6002758 -0.175732020  0.15123239

• We can then see that PC1 is pretty close to the average, across all the exam scores.
• PC2 is a contrast between the closed book and open book exams.
• What do you think PC3 is representing?

• We can also compute the data matrix in the new reference frame - sometimes called the rotated variables

head( v1$x, 4)

##         PC1       PC2        PC3       PC4        PC5
## 1 -66.32077 -6.447125  7.0736275 -9.646383  5.4557651
## 2 -63.61810  6.754424  0.8599283 -9.149064 -7.5656517
## 3 -62.92626 -3.080258 10.2297139 -3.723843 -0.3841125
## 4 -44.53775  5.577218 -4.3780192 -4.481675  4.4065605

• They are uncorrelated

round(cor(v1$x), digits=3)

##     PC1 PC2 PC3 PC4 PC5
## PC1   1   0   0   0   0
## PC2   0   1   0   0   0
## PC3   0   0   1   0   0
## PC4   0   0   0   1   0
## PC5   0   0   0   0   1

v1$sdev

## [1] 26.210490 14.216577 10.185642  9.199481  5.670387

• the largest SD is about 26 and the smallest close to 6
• if the data followed a multivariate Normal distribution (it does not) these would tell us about the size of the ellipses that describe the data

Notice that if we compute the SD for each column we get back the singular values.

apply(v1$x, 2, sd)

##       PC1       PC2       PC3       PC4       PC5 
## 26.210490 14.216577 10.185642  9.199481  5.670387

• Sometimes we have \(p\) columns, but the point cloud really only occupies some lower dimensional space.
• in the rotated coordinate system we only need 2 dimensions, so one of the rotated variables will be zero

x= rnorm(20, sd=10); y=rnorm(20, sd=5); z= x+y

##              x         y         z
## [1,]  5.650028 -8.975037 -3.325009
## [2,]  5.283584 -4.432313  0.851271
## [3,] -8.467440 -1.383923 -9.851363

v2 = prcomp(cbind(x,y,z))
head(v2$x, n=3)

##            PC1       PC2           PC3
## [1,] -3.524702  8.321742  7.841263e-16
## [2,] -6.449121  2.875668  1.914837e-15
## [3,] 10.670849 -1.578135 -2.978531e-15

#round(v2$sdev, digits=3)

• It would be quite unusual in a real world example where the variables are measured with error for us to find a case where one or more of the eigenvalues is exactly zero
• So we use a cut-off of some form that says that if the variability in one direction is smaller than \(\eta\) we don’t think that dimension will be very helpful.
• In our single cell analysis pipeline we will typically choose some fairly large number of PCs (eg 50) to use for clustering the data, this is a form of dimension reduction

• in our example we have test scores as percentages, so this limits the effect of outliers
• just to emphasize the point, I will use some very large values in the last 3 columns

ns = c(1, 2, 850,950, 999)
ndata = rbind(examScor, ns)
tv = prcomp(ndata)
tv$sdev

## [1] 164.724987  19.864187  10.193072   9.771466   6.620818

v1$sdev

## [1] 26.210490 14.216577 10.185642  9.199481  5.670387

tv$rotation

##                    PC1          PC2        PC3         PC4         PC5
## mechanics  -0.01768134 -0.846855292 -0.1026264  0.52058224  0.03139179
## vectors    -0.02536899 -0.530897676  0.1781267 -0.82756977 -0.03005392
## algebra     0.51715704 -0.022016155  0.1892014  0.06919271 -0.83155222
## analysis    0.58650891 -0.006343286  0.6179097  0.10042743  0.51387643
## statistics  0.62257504 -0.021420175 -0.7349348 -0.17102371  0.20630860

v1$rotation

##                   PC1         PC2        PC3          PC4         PC5
## mechanics  -0.5054457 -0.74874751  0.2997888  0.296184264  0.07939388
## vectors    -0.3683486 -0.20740314 -0.4155900 -0.782888173  0.18887639
## algebra    -0.3456612  0.07590813 -0.1453182 -0.003236339 -0.92392015
## analysis   -0.4511226  0.30088849 -0.5966265  0.518139724  0.28552169
## statistics -0.5346501  0.54778205  0.6002758 -0.175732020  0.15123239

• in regression we have a response, \(\bf{y}\) that we want to model in terms of matrix, \(\bf{X}\) of features.
• suppose for example that 10 students had taken the first 4 exams, but had COVID and could not take the 5th. The instructor wants to estimate their score on the 5th exam.

y = examScor[,5]
covs = as.matrix(examScor[,1:4])
lm1 = lm(y~covs)

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)   -11.37822    6.98174  -1.630 0.106952    
covsmechanics   0.02217    0.09895   0.224 0.823265    
covsvectors     0.02574    0.13953   0.184 0.854092    
covsalgebra     0.72944    0.20961   3.480 0.000802 ***
covsanalysis    0.31293    0.13146   2.380 0.019581 *  

Residual standard error: 12.75 on 83 degrees of freedom
Multiple R-squared:  0.4793,    Adjusted R-squared:  0.4542 
F-statistic:  19.1 on 4 and 83 DF,  p-value: 3.612e-11

• now we are going to see what happens if instead of using the data in its original coordinate system we use the PCs

pccovs = prcomp(covs)
lm2 = lm(y~pccovs$x)

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 42.30682    1.35892  31.133  < 2e-16 ***
pccovs$xPC1 -0.45441    0.05918  -7.678 2.84e-11 ***
pccovs$xPC2  0.37633    0.10885   3.457 0.000863 ***
pccovs$xPC3  0.08628    0.14739   0.585 0.559898    
pccovs$xPC4  0.52498    0.23109   2.272 0.025687 *  

Residual standard error: 12.75 on 83 degrees of freedom
Multiple R-squared:  0.4793,    Adjusted R-squared:  0.4542 
F-statistic:  19.1 on 4 and 83 DF,  p-value: 3.612e-11

• the fit of the model (and indeed the residuals, for example) is identical between the two
• the data have not changed, we just changed how we referred to the data in our coordinate system
• in the summary for lm2 we can see that the coefficients for PC3 and PC4 are not significant
• you can check (use the rmarkdown doc for this lesson) that indeed removing them has little impact on the fit
• removing them could provide some benefits in terms of simplicity etc and this is a form of smoothing

lm3 = lm(y~pccovs$x[,1:3])

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        42.30682    1.38665  30.510  < 2e-16 ***
pccovs$x[, 1:2]PC1 -0.45441    0.06039  -7.524 5.08e-11 ***
pccovs$x[, 1:2]PC2  0.37633    0.11107   3.388  0.00107 ** 
---
Residual standard error: 13.01 on 85 degrees of freedom
Multiple R-squared:  0.4448,    Adjusted R-squared:  0.4317 
F-statistic: 34.05 on 2 and 85 DF,  p-value: 1.378e-11

• note that the coefficients are identical to the values for the first model with all PCs included
• for the model fit on the original data the variables are co-linear and removing one affects the estimates for the others

• the principal components are not scale invariant
• changing the scale (pounds to kilograms, scaling by the variance etc) all change the set of principal components we find and hence make interpretation harder

• it is useful at times to determine whether most (all?) of the important variation is contained in the first \(k\) PCs
• under an assumption that we are sampling from a multivariate Normal distribution (strong but typically it doesn’t need to hold that well) we can say some things
• with real data the hypothesis that any singular value (or eigenvalue) is zero is not meaningful, we always have some amount of random variation
• but a test of \(\lambda_p = \lambda_{p-1}\) is meaningful
• as is the more general \(\lambda_p = \lambda_{p-1} = \ldots = \lambda_{k+1}\), which is equivalent to saying that the first \(k\) components hold all the relevant information and after that it is just stochastic noise
• Section 8.4.3 of Mardia Kent and Bibby…but this does not seem to be employed in the single cell field…

• in many different molecular biology assays we collect data on individuals or cells
• that data could be genotypes, gene expression values, methylation etc
• the data structures for these assays are usually transposed from those used above for the exam scores on students
• in biological examples there tend to be many more quantities that are measured than there are individuals
• so for most of these examples we will be working with the transpose of the data matrix
• we are often interested in finding a low dimensional set of features that explain most of the between individual variation

• In a single cell experiment we typically have hundreds to thousands of cells.
• We have 10’s of thousands of genes….
• So each cell is a point in some 10-40K space…but we think that the there are useful summaries
• We want to use PCA analysis as a way to do some form of dimension reduction - to those directions where there is a lot of variation in the data.
• outliers can greatly skew the results and we need to be careful to ensure that our analysis is not too reliant on a few observations.

• get your single cell data and do QA/QC to remove genes (rows) and cells (columns) that seem to have technical or biological reasons to be be suspect
• log transform and size normalize the data
• do some sort of filtering for the top K most variable genes (K and how you measure variable are parameters)
• do PCA
• use the first M PCs to do clustering
• revert back to the full count matrix and use UMAP and tSNE to visualize

• We will examine the single cell data from workflow #3 in the OSCA book
• http://bioconductor.org/books/3.17/OSCA.workflows/
• This is a peripheral blood mononuclear cell (PBMC) dataset from 10X Genomics (Zheng et al. 2017). The data are publicly available from the 10X Genomics website, from which we download the raw gene/barcode count matrices, i.e., before cell calling from the CellRanger pipeline. The data were processed as described in that workflow.

pct_var = attr(reducedDims(sc_exp_norm_trans)$PCA, "percentVar") |> 
   round(digits = 1)
pct_var

##  [1] 16.5  5.9  4.6  2.1  1.3  0.9  0.8  0.7  0.6  0.5  0.4  0.4  0.3  0.3  0.3
## [16]  0.3  0.3  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2
## [31]  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2  0.2
## [46]  0.2  0.2  0.2  0.2  0.2

• notice that after about 12 components we are at .3 and then quickly just .2 over and over
• think back to the earlier comment that it could be useful to test if those later values are all equal…

Here we consider a set of models, one for each PC, where we ask how much of the variation in the PC is explained by the clusters

The variables \(1_{Cj}\) are indicator variables, the \(i^{th}\) element is 1 if the \(i^{th}\) observation is in the \(j^{th}\) cluster and 0 otherwise.

\[ PC_l = \beta_1 \cdot 1_{C1} + \cdots + \beta_k \cdot 1_{Ck} \] We can fit this model for each of some selected number of PCs.

From previous output for this problem above we probably only need to consider the first 10 PCs.

• note that this shows PC1 is essentially a contrast between a subset of the clusters

• note that for PC6 the multiple \(R^2\) is very close to zero, suggesting that the variation in that direction is not described by the clusters

• we note that for the first three PCs the \(R^2\) is larger than \(0.8\), but drops to around \(0.6\) for PC4
• we plot PC4 vs PC1
• what is that set of unusual looking points?

clusters[abs(pcs[,4])>15]

##  [1] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [39] 9 9 9 9 9 9 9
## Levels: 1 2 3 4 5 6 7 8 9 10

table(clusters)

## clusters
##    1    2    3    4    5    6    7    8    9   10 
##  565  511  381  164  353 1020  170  312  115  377

• so we see that within cluster 9 there is a lot of variation in the direction of PC4
  
• to track this down we can ask which genes are highly variable within cluster 9

• we now find the highly variable genes within cluster 9
• so we only use the cluster 9 cells

exprs9 = assays(sc_exp_norm_trans)$logcounts[, clusters==9]
sdbyg9 = rowSds(exprs9)
names(sdbyg9) = row.names(exprs9)
topsdbyg9 = sort(sdbyg9,dec=TRUE)[1:50]
topsdbyg9[1:10]

##      PPBP       PF4     GNG11      SDPR HIST1H2AC      CCL5    MALAT1     TUBB1 
##  3.456763  3.334462  3.000800  2.929638  2.928735  2.841697  2.821872  2.664090 
##      NRGN    TMSB4X 
##  2.660178  2.634199

• some of these are platelet factors, the first is a sign of platelet activation

• we produce a density plot of gene expression values for PPBP within cluster 9

• a parallel coordinate plot is one way of showing very high dimensional data
• each dimension is encoded as a vertical line (so \(k\)-dimensional data has \(k\) lines)
• each point \(\bf{x} = (x_1, x_2, \ldots, x_k)\) is represented as a horizontal line, which joins the values \(x_j\) to \(x_{j+1}\)
• important considerations include
  • order of the axis
  • scaling of each axis
  • any rotations
• one additional issue is that data points that share the same value for all displayed dimensions will occlude each other

• one issue with this plot is that any points that have the same values across all the variables appear as one line
• 50 of the 114 are zero for all four genes
• we rescale the \(y\)-axis by square root - often appropriate for counts

• our regression of the PCs against the cluster identities suggested that in the direction of PC4 there was some variability that was not being explained by the clusters
• we found that most of that variation was contained in one cluster, cluster 9
• when we looked at the highly variable genes in that cluster we found that the top four seemed to be coordinately expressed
• these genes were involved in platelet activation
• but we have not proven anything, and we would want to follow up with a biologist who is knowledgeable about platelets to see if there are further experiments we should carry out

• we now return to the regression analysis and consider PC6, where \(R^2\) was about \(0.03\)
• first plot PC6 vs PC1 and as we see there is no real pattern in the PC6 direction
• we do see the separation in the PC1 direction, but that is not relevant

• now look at boxplots of PC6 split by cluster
• we see no real pattern
• the clusters have very similar values in this direction

• we tested for correlation of PC6 with zero fraction - none
• we tested for correlation with sizeFactor - none
• let’s look at the loadings in the rotation matrix

• above we concentrated on the transformed features
• but the rotations are also interesting
• they tell us about the “loadings” on each of the genes for each PC
• in the exam example we noted how the different rotations were things like the mean of the exam scores, or contrasts between exams

rotation = attr(reducedDims(sc_exp_norm_trans)$PCA, "rotation")
gn = row.names(rotation)
gn[1:10]

##  [1] "FO538757.2"    "AP006222.2"    "RP11-206L10.9" "FAM41C"       
##  [5] "NOC2L"         "HES4"          "ISG15"         "C1orf159"     
##  [9] "TNFRSF18"      "TNFRSF4"

• the rotation tells us how the different features are weighted in creating the sixth PC
• we can see that four genes have very large (relative to the others) weights
• so they are important and likely drive the values in PC6

sort(abs(rotation[,6]),dec=TRUE)[1:10]

##       JUN      JUNB       FOS     DUSP1     ZFP36   TSC22D3      CD69      IER2 
## 0.3525449 0.3474410 0.3425707 0.2952822 0.1790868 0.1725553 0.1649484 0.1632666 
##     CXCR4      BTG2 
## 0.1415743 0.1344974

• first see if the values are all positive, negative or a mixture
• look to see what the most negative loadings are

rotation[c("JUN", "FOS","JUNB","DUSP1"),6]

##       JUN       FOS      JUNB     DUSP1 
## 0.3525449 0.3425707 0.3474410 0.2952822

sort(rotation[,6],dec=FALSE)[1:10]

##        S100A8        S100A9       S100A12           LYZ RP5-1171I10.5 
##   -0.10103262   -0.09712595   -0.06945209   -0.05584233   -0.05426363 
##         RPLP1         GAPDH RP11-1143G9.4          VCAN        LUC7L3 
##   -0.05240460   -0.05169662   -0.05093415   -0.04881408   -0.04481279

• prompt: Do the genes FOS and DUSP1 interact and if so, how?
• redacted output: FOS interacts with JUN and JUNB to form the AP-1 transcription factor complex, which activates DUSP1 and other genes in the list of the ones with high loadings on rotation 6
• FOS is an immediate early gene that encodes a transcription factor known as c-Fos. It is typically induced rapidly in response to various extracellular stimuli, such as growth factors, hormones, and stress signals.
• from a google search: • DOI: 10.1007/s10911-020-09448-1

Here, we show that the AP-1 transcription factor components, i.e. JUN, JUNB, FOS, FOSB, in addition to DUSP1, EGR1, NR4A1, IER2 and BTG2, behave as a conserved co-regulated group of genes whose expression is associated to ZFP36 in cancer cells.

• you could choose some threshold value for PC6 and defined stressed cells as those having a value larger than that threshold
• then you could use this to color cells in your UMAP or t-SNE plots
• and you could imagine using some, potentially scaled in some way, as a signature for cell stress (if we validate that the signal we are seeing in more experiments)

• Irizarry find a strong correlation between PC1 and the fraction of genes not detected

 counts = assays(sc_exp_norm_trans)$counts
 zeroFrac = apply(counts, 2, function(x) sum(x==0))/nrow(counts)
 lmzf = lm(pc1~clusters-1+zeroFrac)

rot1 = rotation[,1]
names(rot1) = row.names(rotation)
rot1sub = rot1[row.names(GBG)]
lmGGO = lm(rot1sub ~ GBG[,1:1000]-1)

• when you do lots of tests it is important that you adjust the p-values to accommodate the fact that you did a lot of tests

ss = summary(lmGGO)
gbg_coefs=coef(ss)
row.names(gbg_coefs) = gsub("GBG[, 1:1000]", "", 
                            row.names(gbg_coefs), fixed=TRUE)
adjpvs = p.adjust(gbg_coefs[,4], method = "fdr")

top10 = sort(adjpvs, dec=FALSE)[1:10]
top10

##   GO:0002523   GO:0000028   GO:0004869   GO:0002468   GO:0002283   GO:0006956 
## 5.569040e-19 5.770953e-18 5.500086e-14 3.641409e-13 1.411069e-11 2.054637e-11 
##   GO:0005767   GO:0005504   GO:0002544   GO:0002548 
## 3.575985e-11 2.118953e-08 2.972449e-08 1.382596e-05

• The top hit is GO:0000028, BP, ribosomal small subunit assembly
• second hit GO:0002523, BP, leukocyte migration involved in inflammatory response

names(top10[1:2])

## [1] "GO:0002523" "GO:0000028"

gset1 = ss4[names(top10)[1]]
gset1

## $`GO:0002523`
##  [1] "S100A9" "S100A9" "S100A8" "S100A8" "SLAMF1" "TNF"    "CCR6"   "NINJ1" 
##  [9] "FUT7"   "JAM3"   "ALOX5"  "ALOX5"  "ADAM8"

gset2 = ss4[names(top10)[2]]
gset2

## $`GO:0000028`
##  [1] "RPS27"  "XRCC5"  "RPSA"   "RPS14"  "RPS14"  "ABT1"   "PRKDC"  "RPS27L"
##  [9] "ERAL1"  "ERAL1"  "MRPS7"  "RPS15"  "RPS28"  "RPS19"  "RPS19"  "RPS5"  
## [17] "RRP7A"

gbg_coefs[names(top10)[1]]

## [1] NA

nn=names(top10)
gensetsize = sapply(ss4[nn], function(x) length(x))
gensetsize

## GO:0002523 GO:0000028 GO:0004869 GO:0002468 GO:0002283 GO:0006956 GO:0005767 
##         13         17         16         12         18         33         20 
## GO:0005504 GO:0002544 GO:0002548 
##         21         11         32

• note this is not what I want to do, but on my laptop about 1.5K is what we can do

rot6 = rotation[,6]
names(rot6) = row.names(rotation)
rot6sub = rot6[row.names(GBG)]
lmGGO6 = lm(rot6sub ~ GBG[,1500:3000])

ss6 = summary(lmGGO6)
gbg6_coefs=coef(ss6)
row.names(gbg6_coefs) = gsub("GBG[, 1500:3000]", "", row.names(gbg6_coefs), fixed=TRUE)
adjpvs6 = p.adjust(gbg6_coefs[,4], method = "fdr")

top10 = sort(adjpvs6, dec=FALSE)[1:10]
top10

##   GO:0035994   GO:0046686   GO:0033687   GO:0036492   GO:0035497   GO:0032570 
## 4.035890e-19 1.401745e-17 6.933533e-16 6.933533e-16 1.689095e-14 2.692586e-12 
##   GO:0036491   GO:0035914   GO:0046697   GO:0050786 
## 1.967677e-11 6.792729e-11 6.788027e-10 1.182803e-08

• you can also use PCA to examine batch effects
• for this example we will use the reference_data and query_data from the scDiagnostics package
• we are going to assume that some batch normalization has been carried out and then we will put them together into a single data set
• compute PCAs and then regress the PCs against the batch indicator
• we want to find linear combinations of the gene expression, so we need to transpose our input matrix since these typically have the genes as rows not columns
• we scale the input columns

• modeling bach effects in single cell experiments is a complex task
• it will be good to familiarize yourself with the OSCA Multisample Book https://bioconductor.org/books/3.19/OSCA.multisample/
• in particular if there are differences in cell type composition between the batches then normalization may impact biology

• in this example we have processed the data from vignette in the batchelor package
• there are two brain data sets (Tasic et al. 2016; Zeisel et al. 2015) from the scRNAseq package.
• A more thorough explanation of each of these steps is available in the OSCA book.
• not that while the tools in batchelor and other packages instantiate a PCA object into the combined single cell experiment these are not the same as a PCA on the assays in that experiment.
• so we compute, and use, that PCA directly (code available in the repo for this document)

## [1] 0.4069 0.4699 0.0059 0.0244 0.0104 0.0018 0.0081 0.0017 0.0230

• we now carry out the same calculation - regress each of the first 9 PCs against batch and report the multiple R^2
• the results are better, but not great
• there are likely many differences between the two datasets, and batch correction isn’t really appropriate
• something more along the lines of data set alignment, perhaps based on cell type specific alignments would be more appropriate

 multR2mnn = sapply(1:9, function(x) {
     summary(lm(pcc$x[,x]~ batch - 1))$adj.r.squared
 })
 round(multR2mnn, digits=4)

## [1]  0.1425  0.0309  0.2271  0.0549  0.0129 -0.0003  0.0301  0.0061  0.0005

• A linear combination of \(\bf{x}\) is \(\sum x_i *l_i\) for some constants \(l_i\)
• The linear combination is a standardized linear combination (SLC) if \(\sum {l_i}^2=1\)

Borrowed from Elements of Statistical Learning… The singular value decomposition (SVD) of the centered input matrix \(Nxp\) matrix \(\bf{X}\)
\[ \bf{X} = \bf{UDV^T}\] Here \(\bf{U}\) and \(\bf{V}\) are \(N × p\) and \(p × p\) orthogonal matrices, with the columns of \(\bf{U}\) spanning the column space of \(\bf{X}\), and the columns of \(\bf{V}\) spanning the row space. \(\bf{D}\) is a \(p × p\) diagonal matrix, with diagonal entries \(d_1 ≥ d_2 ≥ \ldots ≥ d_p ≥ 0\) called the singular values of \(\bf{X}\). If one or more values \(d_j = 0\), \(\bf{X}\) is singular.

The SVD of the centered matrix \(\bf{X}\) is another way of expressing the principal components of the variables in \(\bf{X}\). The sample covariance matrix is given by \(S = X^TX/N\), and we have \[X^T X = VD^2V^T,\] which is the eigen decomposition of \(\bf{X}^T\bf{X}\) (and of \(\bf{S}\), up to a factor \(N\)). The eigenvectors \(v_j\) (columns of \(\bf{V}\)) are also called the principal components directions of \(\bf{X}\). The first principal component direction \(v_1\) has the property that \(z_1 = X \cdot v_1\) has the largest sample variance amongst all normalized linear combinations of the columns of \(\bf{X}\).

somehow lost the doublet code

lmDoublets = lm(pc1~clusters-1+sc_exp_norm_trans$subsets_Mito_percent)